The first attempt to address the high resource consumption of ASPP was FastFCN [1]}, which performs a new method of ascending pyramidal sampling.
Besides, AdapNet++ [2]}, [3]} proposed cascaded and parallel Atrous convolutions to capture long-range context using fewer parameters.
However, the problem of spatial precision loss persisted.
These results lead us to believe that we need models that make use of inductive biases, i.e., more specific features from prior information.
We address this problem by using well-behaved hourglass models paired with multi-task learning to improve the learned features.
